百度去年 AI 业务营收达 400 亿元，萝卜快跑累计出行破 2000 万
近期，有网友发帖称，自己夜间驾驶领克 Z20 时，使用语音助手进行阅读灯关闭，车辆却将大灯等车外灯光关闭，导致道路一片漆黑，最终在高速发生碰撞。
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But that’s unironically a good idea so I decided to try and do it anyways. With the use of agents, I am now developing rustlearn (extreme placeholder name), a Rust crate that implements not only the fast implementations of the standard machine learning algorithms such as logistic regression and k-means clustering, but also includes the fast implementations of the algorithms above: the same three step pipeline I describe above still works even with the more simple algorithms to beat scikit-learn’s implementations. This crate can therefore receive Python bindings and even expand to the Web/JavaScript and beyond. This also gives me the oppertunity to add quality-of-life features to resolve grievances I’ve had to work around as a data scientist, such as model serialization and native integration with pandas/polars DataFrames. I hope this use case is considered to be more practical and complex than making a ball physics terminal app.
This one was a lot better than others. For every SAT problem with 10 variables and 200 clauses it was able to find a valid satisfying assignment. Therefore, I pushed it to test with 14 variables and 100 clauses, and it got half correct among 4 instances (See files with prefix formula14_ in here). Half correct sounds like a decent performance, but it is equivalent to random guessing.